In typical commercial Knowledge Management (KM) is a set of practices that covers identifying, generating, sharing and utilizing knowledge. As an efficient manner, Web-based collaborative tools, such as wiki and blogs, need cutting edge content tagging and search technologies to foster collaborations and knowledge management. These technologies have resulted in substantial improvements in locating, contributing and sharing knowledge.
It has become increasingly difficult to share knowledge or locate the right information and people within organization resources, since we are surrounded with a vast amount of information. As a result, corporations are always interested in managing and sharing intellectual assets, and maintaining the exponentially increasing number of content within an organization.
With an ever increasing amount of content, we heavily rely on search engine to locate documents. However, existing search tools are experiencing difficulties: keyword based search often return results with low precision and recall. An approach for mitigating this issue is to use content tags. Content tagging helps users to describe and organize content as described in “[2] Hak Lae Kim, Alexandre Passant, John G. Breslin, Simon Scerri, and Stefan Decker, “Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces,” in /CSC '08: Proceedings of the 2008 IEEE International Conference on Semantic Computing, Washington, D.C., USA, 2008, pp. 315-322.”
Good tags provide relevant and brief information about resources. The user generated tagging approach has resulted in improvements in locating information; therefore it is getting more popular. Many popular Web sites support tagging (i.e. Delicious, Facebook™, Flickr™ and YouTube™).
As an economical ways to improve content management and search, the user generated tagging has major limitations because it is (1) free from context and form, (2) user generated, (3) used for purposes other than description, and (4) often ambiguous. Since tagging is a subjective, time-consuming voluntary work, most available documents are not tagged at all.
Automatic tagging can overcome some of the above issues by analyzing documents and offer significant terms as tags without user intervention.
Semantic web technologies are seen as the enabler technology for effectively maintaining and retrieving information. Semantic Knowledge Management is a set of practices that maintains data with its metadata in a machine readable format. This approach would leverage usage of intelligent semantic content tagging, indexing and search methods, and would reduce the cost and time to localize content as described in “[1] Wikipedia. Knowledge Management. [Online]. http://en.wikipedia.org/wiki/Knowledge_management; on WWW on Sep. 16, 2013.”
Currently, search results in documents in computer systems based on automatic tagging are not satisfactory. Accordingly novel and improved methods and systems for automatic Semantic tagging and search of documents are required.